Identifying Master’s Program Web Pages with Machine Learning

Díaz Oporto S.; Dueñas Vera S.; Manrique Chalco W.; Bragagnini Mendizábal C.; Fernández Del Carpio A. · Lecture Notes in Educational Technology (2023) · DOI: 10.1007/978-981-99-7353-8_9

Resumen

In the journey of education and professional growth, there is a wide variety of postgraduate programs and specializations offered by different universities and training centers, many of which have a significant online presence. Simultaneously, for the institutions offering these programs, it is of interest to gather information about the competition in order to develop their study offering strategies accordingly. Likewise, for most individuals interested in pursuing these studies, identifying these programs on various published web pages proves to be challenging. This work presents a tool for detecting relevant information from web page content, allowing the identification of master’s programs. One of the primary challenges addressed in this research is the technological issue of accurately and efficiently extracting information from diverse web sources, given the variability in webpage structures and formats. The tool is based on the analysis of various Machine Learning and Deep Learning models, along with the application of web scraping techniques. Tests and validations were conducted on different models, with the best result being achieved by the deep recurrent neural networks model, which reached a 91.95% F1-score metric. The use of automated tools for selection, gathering, and classification of information enhances learning by enabling a greater focus on points of interest. Therefore, when searching for master’s degree programs, people would avoid the tedious task of manual searching and sorting. Instead, they can focus on deeply analyzing and comparing the options. This focus on exploration and analysis would enhance their understanding of the options and promote more informed decisions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

Instituciones: Universidad La Salle